MM_est: Function for estimating the Michaelis-Menten constant In EKMCMC: MCMC Procedures for Estimating Enzyme Kinetics Constants

Description

The function estimates the Michaelis-Menten constant using progress-curve data, enzyme concentrations, substrate concentrations, and the catalytic constant.

Usage

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 MM_est( method, timespan, products, enz, subs, catal, K_M_init, std, tun, nrepeat, jump, burn, K_M_m, K_M_v, volume, t_unit, c_unit )

Arguments

 method This determines which model, the sQSSA or tQSSA model, is used for the estimation. Specifically, the input for method is TRUE (FALSE); then the tQSSA (sQSSA) model is used. timespan time points when the concentrations of products were measured. products measured concentrations of products enz initial enzyme concentrations subs initial substrate concentrations catal true value of the catalytic constant. K_M_init initial value of K_M constant for the Metropolis-Hastings algorithm. If the input is FALSE then it is determined by max(subs). std standard deviation of proposal distribution. If the input is FALSE then it is determined by using the hessian of log posterior distribution. tun tuning constant for the Metropolis-Hastings algorithm when std is FALSE (i.e., hessian of the log posterior distribution is used). nrepeat number of effective iteration, i.e., posterior samples. jump length of distance between sampling, i.e., thinning rate. burn length of burn-in period. K_M_m prior mean of gamma prior for the Michaelis-Menten constant K_M. If the input is FALSE then it is determined by max(subs). K_M_v prior variance of gamma prior for the Michaelis-Menten constant K_M. If the input is FALSE then it is determined by max(subs)^2*1000. volume the volume of a system. It is used to scale the product concentration. FALSE input provides automatic scaling. t_unit the unit of time points. It can be an arbitrary string. c_unit the unit of concentrations. It can be an arbitrary string.

Details

The function MM_est generates a set of Markov Chain Monte Carlo simulation samples from posterior distribution of the Michaelis-Menten constant of enzyme kinetics model. Because the function estimates only the Michaelis-Menten constant the true value of the catalytic constant should be given. Authors' recommendation: "Do not use this function directly. Do use the function main_est() to estimate the parameter so that the main function calls this function"

Value

A vector containing posterior samples of the estimated parameter: the Michaelis-Menten constant.

Examples

 1 2 3 4 5 6 7 8 9 10 11 ## Not run: data("timeseries_data_example") timespan1=,c(1,3,5,7)] products1=,c(2,4,6,8)] MM_result <- MM_est(method=TRUE,timespan=timespan1,products=products1, enz = c(4.4, 4.4, 440, 440), subs=c(4.4, 4.4, 4.4, 4.4), catal = 0.051, K_M_init = 1, K_M_m = 1, K_M_v = 100000, std = 10, tun =3.5, nrepeat = 1000, jump = 10, burn = 1000, volume = FALSE, t_unit = "sec", c_unit = "mM") ## End(Not run)

EKMCMC documentation built on Aug. 20, 2021, 9:08 a.m.